





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				UpperCAmelCase		:  Any         =	argparse.ArgumentParser(
				    description=(
				        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
				        ''' Distillation'''
				    )
				)
				parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
				parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
				parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
				parser.add_argument('''--vocab_transform''', action='''store_true''')
				UpperCAmelCase		:  Dict         =	parser.parse_args()

				if args.model_type == "bert":
								UpperCAmelCase		:  Dict         =	BertForMaskedLM.from_pretrained(args.model_name)
								UpperCAmelCase		:  Optional[Any]         =	'''bert'''
				else:
								raise ValueError('''args.model_type should be \"bert\".''')

				UpperCAmelCase		:  List[str]         =	model.state_dict()
				UpperCAmelCase		:  str         =	{}

				for w in ["word_embeddings", "position_embeddings"]:
								UpperCAmelCase		:  int         =	state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								UpperCAmelCase		:  str         =	state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				UpperCAmelCase		:  str         =	0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												UpperCAmelCase		:  int         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												UpperCAmelCase		:  List[Any]         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												UpperCAmelCase		:  int         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												UpperCAmelCase		:  int         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												UpperCAmelCase		:  str         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												UpperCAmelCase		:  Any         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												UpperCAmelCase		:  Optional[Any]         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												UpperCAmelCase		:  Optional[Any]         =	state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				UpperCAmelCase		:  Any         =	state_dict['''cls.predictions.decoder.weight''']
				UpperCAmelCase		:  Tuple         =	state_dict['''cls.predictions.bias''']
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												UpperCAmelCase		:  Optional[int]         =	state_dict[F"""cls.predictions.transform.dense.{w}"""]
												UpperCAmelCase		:  Any         =	state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)











'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



